Cyberbullying Detection In Twitter Using Sentiment Analysis

Chong, Poh Theng and Othman, Nur Fadzilah and Abdullah, Raihana Syahirah and Anawar, Syarulnaziah and Ayop, Zakiah and Ramli, Sofia Najwa (2021) Cyberbullying Detection In Twitter Using Sentiment Analysis. International Journal of Computer Science and Network Security, 21 (11). pp. 1-10. ISSN 1738-7906

Text 2.3.1 CYBERBULLYING DETECTION IN TWITTER USING SENTIMENT ANALYSIS IJCSNS.PDF Download (847kB)

Abstract

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

Item Type:	Article
Uncontrolled Keywords:	Sentiment analysis, Cyberbullying, Twitter, Machine learning
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	16 Mar 2022 11:28
Last Modified:	16 Mar 2022 11:28
URI:	http://eprints.utem.edu.my/id/eprint/25752
Statistic Details:	View Download Statistic

Actions (login required)

View Item